A Means-End Investigation of Ropes Course Experiences

Goldenberg, Marni; Klenosky, David B.; O’Leary, Joseph T.; Templin, Thomas J.

doi:10.1080/00222216.2000.11949914

Cited by 104 publications

(133 citation statements)

References 20 publications

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“…Several researchers (e.g. Botschen & Hemetsberger, 1998;Goldenberg et al, 2000) employ paper-and pencil versions successfully in their studies. Our study uses an online version of the 'hard' paper-andpencil design instead of conducting personal interviews.…”

Section: Laddering Approach and Means-end Theorymentioning

confidence: 99%

Complaint management expectations: An online laddering analysis of small versus large firms

Henneberg

Gruber

Reppel

et al. 2009

Industrial Marketing Management

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Section: Laddering Approach and Means-end Theorymentioning

confidence: 99%

Complaint management expectations: An online laddering analysis of small versus large firms

Henneberg

Gruber

Reppel

et al. 2009

Industrial Marketing Management

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“…A consumption consequence can be positive or negative. Most researchers agree that positive feelings are the result of benefits from consuming a product; therefore, benefit can be considered a substitute for consequence in a conventional MEC linkage (Goldenberg, Klenosky, O'Leary, & Templin, 2000;Lin, 2003;Vriens & ter Hofstede, 2000;Woodside, 2004). Furthermore, value can be considered a product of satisfaction based on consumption behaviors or actions (Valette-Florence, 1998).…”

mentioning

confidence: 99%

“…Without the need to predetermine the hierarchical relationships of A-C-V variables, the abstract ratio can be used to determine the A-C-V hierarchies (Goldenberg et al, 2000;Langerak et al, 1999;Orsingher & Marzocchi, 2003). The abstract ratio is acquired by computing the connections between variables.…”

mentioning

confidence: 99%

“…To enhance the advantages of MECs and utilization of a computer information system for storing and analyzing consumer product knowledge, this study integrates the system interface and human consumption behavior to obtain objective information for real consumption demands derived from the computer system. consumption, whereas negative feelings can be regarded as associated cost or perceived risks (Goldenberg et al, 2000). Benefits are related to consumer attribute preference and consumer-perceived product quality (Snelders & Schoormans, 2004).…”

mentioning

confidence: 99%

“…In MEC research, an HVM is employed to develop marketing strategies (Bourne & Jenkins, 2005;Goldenberg et al, 2000;Lin, 2003;Orsingher & Marzocchi, 2003;Woodside, 2004). When the laddering technique is used to construct an HVM, all interview data should be input into a summary implication matrix (SIM; Goldenberg et al, 2000;Langerak, Peelen, & Nijssen, 1999;Lin, 2003;Orsingher & Marzocchi, 2003).…”

mentioning

confidence: 99%

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Systematic behavior research for understanding consumer decision making

Lin

2009

Behavior Research Methods

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Business survival in dynamic and competitive environments depends on an ability to analyze consumption information and determine which products consumers want. Collecting consumption data, hence, becomes a primary activity for product and marketing strategy development. Businesses typically gather consumption data from consumers shopping in the retail market. Data for items purchased are transmitted through retail information systems to inventory controllers for product-ordering decisions. Although such decisions may reflect consumer behavior in the retail market, they cannot reflect consumer desires and wants. Since consumers can purchase only what retailers offer, consumer purchases represent consumer product choices only for limited offerings, rather than consumer needs and expectations. Thus, understanding consumer needs and wants, as well as online consumer behavior, is a significant issue for businesses wanting to provide customers with the best value (Stibel, 2005;Wisniewski, 2001).The means-end chain (MEC) methodology is typically applied to arrive at consumer value satisfaction (Gutman, 1982). Reynolds and Gutman (1988) adopted the logic of MECs to develop a laddering technique for understanding consumer product cognition. When a cutoff value is determined, the salient attribute-consequence-value ( A-C-V ) linkages, by computing the frequency of consumer A-C-V linkages for a given product, can be determined and displayed in a hierarchical value map (HVM). Marketers can use HVMs to generate product, advertising, and segmentation strategies. This study, which is based on that by Reynolds and Olson (2001), applies the MEC methodology to the analysis and understanding of consumer decision making and adopts dynamic programming (DP) analysis to enhance the computing procedure in the conventional laddering technique. Figure 1 exhibits the conceptual framework of the DP approach. In laddering data collection, all data are input into a table for the summary implication matrix (SIM). The DP and systematic procedures can transform the laddering data into meaningful information for consumer product cognition. Such information can assist marketers in understanding consumer perceptions and in developing effective marketing strategies.In the traditional MEC methodology, the cutoff value is predetermined subjectively by the researcher in order to establish an HVM. In the present study, DP analysis overcomes the controversy associated with subjective cutoff value decisions and is the basis for establishing the information system, which stores consumer preference perceptions of a particular product. The following is a list of study objectives.1. To capitalize on the advantages of DP in analyzing MEC data sets, this study integrates DP and laddering procedures to gather consumer preference information.2. This study uses an information flowchart to demonstrate the logic of DP, leading to an analysis of consumer decision making. Such an analysis will provide marketers with salient market intelligence for rapid response to m...

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Applying means‐end chains theory to understanding psychological cognitive structure of online video sharing platforms: A study of user behavior in TikTok

Lin

2023

J of Consumer Behaviour

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The means‐end chains theory was used in this study to explore the attributes most valued by TikTok users and to examine the resulting psychological values generated. Laddering interviews were used for content analysis and “online video sharing platform attributes—user consequences—personal values” psychological cognitive structures, from a perspective of the user were constructed using the structural implication matrix and hierarchical value map. A total of 34 elements were identified from the results of the study. The categorization of attribute elements was also expanded to include motivation for social media communication and interaction, which have not been previously explored. The results showed that diverse video genre, interesting video content, easy‐to‐use interface, visual effects application, and community sharing and recommendation are key platform attributes valued by users. The facilitating observational learning, promoting the joy of life, raising convenience, having fun with friends, relieving emotional stress, and other user consequences were generated by platform attributes. And lastly, personal values such as a sense of accomplishment, fun and enjoyment of life, warm relationships with others, and sense of belonging were gained. In addition, the study also finds that there are differences in the psychological cognitive structures between genders and that the correlations and influences among the elements have different degrees of significance. These findings can give marketers and policymakers a better understanding of the behavior of TikTok users and help them develop better strategies for marketing and promotion of the platform.

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A Means-End Investigation of Ropes Course Experiences

Cited by 104 publications

References 20 publications

Complaint management expectations: An online laddering analysis of small versus large firms

Complaint management expectations: An online laddering analysis of small versus large firms

Systematic behavior research for understanding consumer decision making

Applying means‐end chains theory to understanding psychological cognitive structure of online video sharing platforms: A study of user behavior in TikTok

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